GSE129611 dataset

Combined dataset

This file is used to generate a dataset containing all individual datasets, without melanocytes.

library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)

.libPaths()

## [1] "/usr/local/lib/R/library"

Preparation

In this section, we set the global settings of the analysis. We will store data there :

save_name = "takahashi"
out_dir = "."
n_threads = 5 # for UMAP

We load the sample information :

sample_info = readRDS(paste0(out_dir, "/../1_metadata/takahashi_sample_info.rds"))
project_names_oi = sample_info$project_name

graphics::pie(rep(1, nrow(sample_info)),
              col = sample_info$color,
              labels = sample_info$project_name)

Here are custom colors for each cell type :

color_markers = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_color_markers.rds"))

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

We load the markers and specific colors for each cell type :

cell_markers = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_cell_markers.rds"))
lengths(cell_markers)

##          CD4 T cells          CD8 T cells     Langerhans cells 
##                   13                   13                    9 
##          macrophages              B cells              cuticle 
##                   10                   16                   15 
##               cortex              medulla                  IRS 
##                   16                   10                   16 
##        proliferative               HF-SCs            IFE basal 
##                   20                   17                   16 
## IFE granular spinous                  ORS          melanocytes 
##                   17                   15                   10 
##            sebocytes 
##                    8

We load markers to display on the dotplot :

dotplot_markers = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_dotplot_markers.rds"))
dotplot_markers

## $`CD4 T cells`
## [1] "PTPRC" "CD3E"  "CD4"  
## 
## $`CD8 T cells`
## [1] "CD3E" "CD8A"
## 
## $`Langerhans cells`
## [1] "CD207" "CPVL" 
## 
## $macrophages
## [1] "TREM2" "MSR1" 
## 
## $`B cells`
## [1] "CD79A" "CD79B"
## 
## $cuticle
## [1] "MSX2"  "KRT32" "KRT35"
## 
## $cortex
## [1] "KRT31" "PRR9" 
## 
## $medulla
## [1] "BAMBI"   "ADLH1A3"
## 
## $IRS
## [1] "KRT71" "KRT73"
## 
## $proliferative
## [1] "TOP2A" "MCM5"  "TK1"  
## 
## $`HF-SCs`
## [1] "KRT14"  "CXCL14"
## 
## $`IFE basal`
## [1] "COL17A1" "KRT15"  
## 
## $`IFE granular spinous`
## [1] "SPINK5" "KRT1"  
## 
## $ORS
## [1] "KRT16" "KRT6C"
## 
## $melanocytes
## [1] "DCT"   "MLANA"
## 
## $sebocytes
## [1] "CLMP"  "PPARG"

Make takahashi dataset

Individual datasets

For each sample, we :

• load individual dataset
• look at cell annotation

We load individual datasets :

sobj_list = lapply(project_names_oi, FUN = function(one_project_name) {
  subsobj = readRDS(paste0(out_dir, "/../2_individual/datasets/",
                           one_project_name, "_sobj_filtered.rds"))
  return(subsobj)
})
names(sobj_list) = project_names_oi

lapply(sobj_list, FUN = dim) %>%
  do.call(rbind, .) %>%
  rbind(., colSums(.))

##              [,1] [,2]
## GSM3717034  10320   71
## GSM3717035  12129  275
## GSM3717036  14170  510
## GSM3717037  32458 4084
## GSM3717038  32458 1094
##            101535 6034

We represent cells in the UMAP :

name2D = "RNA_pca_20_umap"

We look at cell type annotation for each dataset :

plot_list = lapply(sobj_list, FUN = function(one_sobj) {
  mytitle = as.character(unique(one_sobj$project_name))
  mysubtitle = ncol(one_sobj)
  
  p = Seurat::DimPlot(one_sobj, group.by = "cell_type",
                      reduction = name2D) +
    ggplot2::scale_color_manual(values = color_markers,
                                breaks = names(color_markers),
                                name = "Cell Type") +
    ggplot2::labs(title = mytitle,
                  subtitle = paste0(mysubtitle, " cells")) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5)) +
    Seurat::NoAxes()
  
  return(p)
})

plot_list[[length(plot_list) + 1]] = patchwork::guide_area()

patchwork::wrap_plots(plot_list, ncol = 3) +
  patchwork::plot_layout(guides = "collect") &
  ggplot2::theme(legend.position = "right")

and clustering :

plot_list = lapply(sobj_list, FUN = function(one_sobj) {
  mytitle = as.character(unique(one_sobj$project_name))
  mysubtitle = ncol(one_sobj)
  
  p = Seurat::DimPlot(one_sobj, group.by = "seurat_clusters",
                      reduction = name2D, label = TRUE) +
    ggplot2::labs(title = mytitle,
                  subtitle = paste0(mysubtitle, " cells")) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5)) +
    Seurat::NoAxes() + Seurat::NoLegend()
  
  return(p)
})

patchwork::wrap_plots(plot_list, ncol = 3)

Drop-Seq vs 10X

This dataset gathers 3 samples obtained with Drop-Seq and 2 obtained with 10X. They may not contain the same genes. We compare the gene identifiers between the two 10X datasets :

ggvenn::ggvenn(data = list(GSM3717037 = sobj_list[["GSM3717037"]]@assays[["RNA"]]@meta.features$Ensembl_ID,
                           GSM3717038 = sobj_list[["GSM3717038"]]@assays[["RNA"]]@meta.features$Ensembl_ID),
               stroke_size = 0.5, set_name_size = 4) +
  ggplot2::labs(title = "Gene Ensembl IDs between the two 10X datasets") +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"))

The two 10X datasets contain the same gene IDs. What about with the three Drop-Seq datasets ?

Note: With the ggvenn package, this is not possible to make a Venn diagram with 5 sets.

ggvenn::ggvenn(data = list(GSM3717034 = sobj_list[["GSM3717034"]]@assays[["RNA"]]@meta.features$Ensembl_ID,
                           GSM3717035 = sobj_list[["GSM3717035"]]@assays[["RNA"]]@meta.features$Ensembl_ID,
                           GSM3717036 = sobj_list[["GSM3717036"]]@assays[["RNA"]]@meta.features$Ensembl_ID,
                           GSM3717037 = sobj_list[["GSM3717037"]]@assays[["RNA"]]@meta.features$Ensembl_ID),
               stroke_size = 0.5, set_name_size = 4) +
  ggplot2::labs(title = "Gene Ensembl IDs between the three Drop-Seq datasets and one 10X") +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"))

There are much less genes detected with the Drop-Seq method. We keep only the genes whose Ensembl IDs are shared between all datasets. When we built individual datasets, we used the same gene annotation. So, genes with the same Ensembl IDs across all datasets have the same names. So, we can work with gene names.

common_genes = Reduce(x = lapply(sobj_list, FUN = rownames),
                      f = intersect)
length(common_genes)

## [1] 7854

It discards a lot of information regarding 10X datasets. So, we keep all common genes between all datasets + common genes between the two 10X datasets.

common_genes = intersect(rownames(sobj_list$GSM3717037), rownames(sobj_list$GSM3717038)) %>%
  c(common_genes, .) %>%
  unique()

length(common_genes)

## [1] 32458

We subset all Seurat objects to keep only these genes :

sobj_list = lapply(sobj_list, FUN = function(one_sobj) {
  avail_genes = intersect(rownames(one_sobj), common_genes)
  one_sobj = one_sobj[avail_genes, ]
  
  return(one_sobj)
})

lapply(sobj_list, FUN = dim) %>%
  do.call(rbind, .) %>%
  rbind(., colSums(.)) %>%
  `colnames<-`(c("Nb genes", "Nb cells"))

##            Nb genes Nb cells
## GSM3717034     9118       71
## GSM3717035    10954      275
## GSM3717036    12218      510
## GSM3717037    32458     4084
## GSM3717038    32458     1094
##               97206     6034

Melanocytes remomal

For each individual dataset, we remove melanocytes. First, we smooth cell type annotation at a cluster level :

sobj_list = lapply(sobj_list, FUN = function(one_sobj) {
  cluster_type = table(one_sobj$cell_type, one_sobj$seurat_clusters) %>%
    prop.table(., margin = 2) %>%
    apply(., 2, which.max)
  cluster_type = setNames(nm = names(cluster_type),
                          levels(one_sobj$cell_type)[cluster_type])
  
  one_sobj$cluster_type = cluster_type[one_sobj$seurat_clusters]
  
  ## Output
  return(one_sobj)
})

To locate melanocytes, we look at their score, cell type annotation, and clustering.

plot_list = lapply(sobj_list, FUN = function(one_sobj) {
  project_name = as.character(unique(one_sobj$project_name))
  plot_sublist = list()
  
  # Score
  plot_sublist[[1]] = Seurat::FeaturePlot(one_sobj, reduction = name2D,
                                          features = "score_melanocytes") +
    ggplot2::labs(title = project_name,
                  subtitle = "Melanocytes score") +
    Seurat::NoAxes() +
    ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.subtitle = element_text(hjust = 0.5))
  
  # Cell type
  plot_sublist[[2]] = Seurat::DimPlot(one_sobj,
                                      reduction = name2D,
                                      group.by = "cell_type",
                                      order = "melanocytes") +
    ggplot2::scale_color_manual(values = c("purple", rep("gray92", length(color_markers) - 1)),
                                breaks = c("melanocytes", setdiff(names(color_markers), "melanocytes"))) +
    ggplot2::labs(title = "Cell type annotation",
                  subtitle = paste0(sum(one_sobj$cell_type == "melanocytes"),
                                    " melanocytes")) +
    Seurat::NoAxes() + Seurat::NoLegend() +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5))
  
  # Clusters
  plot_sublist[[3]] = Seurat::DimPlot(one_sobj,
                                      reduction = name2D,
                                      group.by = "seurat_clusters",
                                      label = TRUE) +
    ggplot2::labs(title = "Clusters") +
    Seurat::NoAxes() + Seurat::NoLegend() +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5))
  
  # Cluster type
  plot_sublist[[4]] = Seurat::DimPlot(one_sobj,
                                      reduction = name2D,
                                      group.by = "cluster_type") +
    ggplot2::scale_color_manual(values = c("purple", rep("gray92", length(color_markers) - 1)),
                                breaks = c("melanocytes", setdiff(names(color_markers), "melanocytes"))) +
    ggplot2::labs(title = "Cluster annotation",
                  subtitle = paste0(sum(one_sobj$cluster_type == "melanocytes"),
                                    " melanocytes")) +
    Seurat::NoAxes() + Seurat::NoLegend() +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5))
  
  return(plot_sublist)
}) %>% unlist(., recursive = FALSE)

patchwork::wrap_plots(plot_list, ncol = 4)

We remove melanocytes based on cluster annotation for 10X datasets and based on the cell type annotation for Drop-Seq datasets :

sobj_list = lapply(sobj_list, FUN = function(one_sobj) {
  if (one_sobj@project.name %in% c("GSM3717037", "GSM3717038")) {
    one_sobj$is_of_interest = (one_sobj$cluster_type != "melanocytes")
  } else {
    one_sobj$is_of_interest = (one_sobj$cell_type != "melanocytes")
  }
  
  if (sum(one_sobj$is_of_interest) > 0) {
    one_sobj = subset(one_sobj, is_of_interest == TRUE)
  } else {
    one_sobj = NA
  }
  
  one_sobj$is_of_interest = NULL
  return(one_sobj)
})

lapply(sobj_list, FUN = dim) %>%
  do.call(rbind, .) %>%
  rbind(., colSums(.))

##             [,1] [,2]
## GSM3717034  9118   65
## GSM3717035 10954  270
## GSM3717036 12218  497
## GSM3717037 32458 3781
## GSM3717038 32458 1023
##            97206 5636

Re-annotation

We remove melanocytes from annotation :

cell_markers = cell_markers[names(cell_markers) != "melanocytes"]
color_markers = color_markers[names(color_markers) != "melanocytes"]
dotplot_markers = dotplot_markers[names(dotplot_markers) != "melanocytes"]

We re-annotate cells for cell type, since melanocytes have been removed :

sobj_list = lapply(sobj_list, FUN = function(one_sobj) {
  # Remove old annotation
  one_sobj@meta.data[, grep(colnames(one_sobj@meta.data), pattern = "score", value = TRUE)] = NULL
  
  # Re-annot
  one_sobj = aquarius::cell_annot_custom(one_sobj,
                                         newname = "cell_type",
                                         markers = cell_markers,
                                         use_negative = TRUE,
                                         add_score = FALSE,
                                         verbose = TRUE)
  
  # Set factor levels
  one_sobj$cell_type = factor(one_sobj$cell_type, levels = names(cell_markers))
  
  return(one_sobj)
})

Combined dataset

We combine all datasets :

sobj = base::merge(sobj_list[[1]],
                   y = sobj_list[c(2:length(sobj_list))],
                   add.cell.ids = names(sobj_list))
sobj

## An object of class Seurat 
## 32458 features across 5636 samples within 1 assay 
## Active assay: RNA (32458 features, 0 variable features)

We add again the correspondence between gene names and gene ID. We take the correspondence from one individual 10X dataset.

sobj@assays$RNA@meta.features = sobj_list[[5]]@assays$RNA@meta.features[, c("Ensembl_ID", "gene_name")]

head(sobj@assays$RNA@meta.features)

##                   Ensembl_ID    gene_name
## MIR1302-10   ENSG00000259553   MIR1302-10
## FAM138A      ENSG00000237613      FAM138A
## OR4F5        ENSG00000186092        OR4F5
## RP11-34P13.7 ENSG00000238009 RP11-34P13.7
## RP11-34P13.8 ENSG00000239945 RP11-34P13.8
## AL627309.1   ENSG00000237683   AL627309.1

We remove the list of objects :

rm(sobj_list)

We keep a subset of meta.data and reset levels :

sobj@meta.data = sobj@meta.data[, c("orig.ident", "nCount_RNA", "nFeature_RNA", "log_nCount_RNA",
                                    "project_name", "sample_identifier", "sample_type",
                                    "laboratory", "location", "Seurat.Phase", "cyclone.Phase",
                                    "percent.mt", "percent.rb", "cell_type")]

sobj$orig.ident = factor(sobj$orig.ident, levels = levels(sample_info$project_name))
sobj$project_name = factor(sobj$project_name, levels = levels(sample_info$project_name))
sobj$sample_identifier = factor(sobj$sample_identifier, levels = levels(sample_info$sample_identifier))
sobj$sample_type = factor(sobj$sample_type, levels = unique(sample_info$sample_type))
sobj$cell_type = factor(sobj$cell_type, levels = names(color_markers))

summary(sobj@meta.data)

##       orig.ident     nCount_RNA     nFeature_RNA    log_nCount_RNA  
##  GSM3717034:  65   Min.   :  269   Min.   : 206.0   Min.   : 5.762  
##  GSM3717035: 270   1st Qu.: 2338   1st Qu.: 918.8   1st Qu.: 7.772  
##  GSM3717036: 497   Median : 7776   Median :2229.0   Median : 8.959  
##  GSM3717037:3781   Mean   : 9513   Mean   :2108.6   Mean   : 8.624  
##  GSM3717038:1023   3rd Qu.:14035   3rd Qu.:2977.0   3rd Qu.: 9.549  
##                    Max.   :48372   Max.   :6334.0   Max.   :10.787  
##                                                                     
##      project_name       sample_identifier sample_type  laboratory       
##  GSM3717034:  65   Takahashi_HD_1:  65    HD:5636     Length:5636       
##  GSM3717035: 270   Takahashi_HD_2: 270                Class :character  
##  GSM3717036: 497   Takahashi_HD_3: 497                Mode  :character  
##  GSM3717037:3781   Takahashi_HD_4:3781                                  
##  GSM3717038:1023   Takahashi_HD_5:1023                                  
##                                                                         
##                                                                         
##    location         Seurat.Phase       cyclone.Phase        percent.mt    
##  Length:5636        Length:5636        Length:5636        Min.   : 0.000  
##  Class :character   Class :character   Class :character   1st Qu.: 1.961  
##  Mode  :character   Mode  :character   Mode  :character   Median : 3.007  
##                                                           Mean   : 3.668  
##                                                           3rd Qu.: 4.196  
##                                                           Max.   :19.934  
##                                                                           
##    percent.rb                    cell_type   
##  Min.   : 1.381   HF-SCs              :1335  
##  1st Qu.:20.621   IFE granular spinous:1321  
##  Median :25.107   IFE basal           : 839  
##  Mean   :24.076   ORS                 : 716  
##  3rd Qu.:28.864   proliferative       : 301  
##  Max.   :48.039   IRS                 : 236  
##                   (Other)             : 888

Processing

We remove genes that are expressed in less than 5 cells :

sobj = aquarius::filter_features(sobj, min_cells = 5)

## [1] 32458  5636
## [1] 18588  5636

sobj

## An object of class Seurat 
## 18588 features across 5636 samples within 1 assay 
## Active assay: RNA (18588 features, 0 variable features)

Metadata

How many cells by sample ?

table(sobj$project_name)

## 
## GSM3717034 GSM3717035 GSM3717036 GSM3717037 GSM3717038 
##         65        270        497       3781       1023

We represent this information as a barplot :

aquarius::plot_barplot(df = table(sobj$project_name,
                                  sobj$cell_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_fill()) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type")

This is the same barplot with another position :

aquarius::plot_barplot(df = table(sobj$project_name,
                                  sobj$cell_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_stack()) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type")

Projection

We normalize the count matrix for remaining cells and select highly variable features :

sobj = Seurat::NormalizeData(sobj,
                             normalization.method = "LogNormalize")
sobj = Seurat::FindVariableFeatures(sobj, nfeatures = 2000)
sobj = Seurat::ScaleData(sobj)

sobj

## An object of class Seurat 
## 18588 features across 5636 samples within 1 assay 
## Active assay: RNA (18588 features, 2000 variable features)

We perform a PCA :

sobj = Seurat::RunPCA(sobj,
                      assay = "RNA",
                      reduction.name = "RNA_pca",
                      npcs = 100,
                      seed.use = 1337L)
sobj

## An object of class Seurat 
## 18588 features across 5636 samples within 1 assay 
## Active assay: RNA (18588 features, 2000 variable features)
##  1 dimensional reduction calculated: RNA_pca

We choose the number of dimensions such that they summarize 60 % of the variability :

stdev = sobj@reductions[["RNA_pca"]]@stdev
stdev_prop = cumsum(stdev)/sum(stdev)
ndims = which(stdev_prop > 0.60)[1]
ndims

## [1] 41

We can visualize this on the elbow plot :

elbow_p = Seurat::ElbowPlot(sobj, ndims = 100, reduction = "RNA_pca") +
  ggplot2::geom_point(x = ndims, y = stdev[ndims], col = "red")
x_text = ggplot_build(elbow_p)$layout$panel_params[[1]]$x$get_labels() %>% as.numeric()
elbow_p = elbow_p +
  ggplot2::scale_x_continuous(breaks = sort(c(x_text, ndims)), limits = c(0, 100))
x_color = ifelse(ggplot_build(elbow_p)$layout$panel_params[[1]]$x$get_labels() %>%
                   as.numeric() %>% round(., 2) == round(ndims, 2), "red", "black")
elbow_p = elbow_p +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.x = element_text(color = x_color))

elbow_p

We generate a UMAP and a UMAP with 41 principal components :

sobj = Seurat::RunTSNE(sobj,
                       reduction = "RNA_pca",
                       dims = 1:ndims,
                       seed.use = 1337L,
                       num_threads = n_threads, # Rtsne::Rtsne option
                       check_duplicates = FALSE,
                       reduction.name = paste0("RNA_pca_", ndims, "_tsne"))

sobj = Seurat::RunUMAP(sobj,
                       reduction = "RNA_pca",
                       dims = 1:ndims,
                       seed.use = 1337L,
                       reduction.name = paste0("RNA_pca_", ndims, "_umap"))

(Time to run : 25.22 s)

We can visualize the two representations :

tsne = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("RNA_pca_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 legend.position = "none")

umap = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("RNA_pca_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

tsne | umap

Batch-effect correction

We remove sample specific effect on the pca using Harmony :

`%||%` = function(lhs, rhs) {
  if (!is.null(x = lhs)) {
    return(lhs)
  } else {
    return(rhs)
  }
}

set.seed(1337L)
sobj = harmony::RunHarmony(object = sobj,
                           group.by.vars = "project_name",
                           plot_convergence = TRUE,
                           reduction = "RNA_pca",
                           assay.use = "RNA",
                           reduction.save = "harmony",
                           max.iter.harmony = 50,
                           project.dim = FALSE)

(Time to run : 19 s)

From this batch-effect removed projection, we generate a UMAP and a UMAP.

sobj = Seurat::RunUMAP(sobj, 
                       seed.use = 1337L,
                       dims = 1:ndims,
                       reduction = "harmony",
                       reduction.name = paste0("harmony_", ndims, "_umap"),
                       reduction.key = paste0("harmony_", ndims, "umap_"))

sobj = Seurat::RunTSNE(sobj,
                       dims = 1:ndims,
                       seed.use = 1337L,
                       num_threads = n_threads, # Rtsne::Rtsne option
                       check_duplicates = FALSE,
                       reduction = "harmony",
                       reduction.name = paste0("harmony_", ndims, "_tsne"),
                       reduction.key = paste0("harmony", ndims, "tsne_"))

(Time to run : 25.3 s)

We visualize the corrected projections :

tsne = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("harmony_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - harmony - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 legend.position = "none")

umap = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("harmony_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - harmony - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

tsne | umap

We will keep the tSNE from harmony :

reduction = "harmony"
name2D = paste0("harmony_", ndims, "_tsne")

Clustering

We generate a clustering :

sobj = Seurat::FindNeighbors(sobj, reduction = reduction, dims = 1:ndims)
sobj = Seurat::FindClusters(sobj, resolution = 1.5)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 5636
## Number of edges: 226335
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8389
## Number of communities: 25
## Elapsed time: 0 seconds

clusters_plot = Seurat::DimPlot(sobj, reduction = name2D, label = TRUE) +
  Seurat::NoAxes() + Seurat::NoLegend() +
  ggplot2::labs(title = "Clusters ID") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))
clusters_plot

Visualization

We represent the 4 quality metrics :

plot_list = Seurat::FeaturePlot(sobj, reduction = name2D,
                                combine = FALSE, pt.size = 0.25,
                                features = c("percent.mt", "percent.rb", "log_nCount_RNA", "nFeature_RNA"))
plot_list = lapply(plot_list, FUN = function(one_plot) {
  one_plot +
    Seurat::NoAxes() +
    ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
    ggplot2::theme(aspect.ratio = 1)
})

patchwork::wrap_plots(plot_list, nrow = 1)

Clusters

We can represent clusters, split by sample of origin :

plot_list = aquarius::plot_split_dimred(sobj,
                                        reduction = name2D,
                                        split_by = "project_name",
                                        group_by = "seurat_clusters",
                                        split_color = setNames(sample_info$color,
                                                               nm = sample_info$project_name),
                                        group_color = aquarius::gg_color_hue(length(levels(sobj$seurat_clusters))))

plot_list[[length(plot_list) + 1]] = clusters_plot +
  ggplot2::labs(title = "Cluster ID") &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, size = 15))

patchwork::wrap_plots(plot_list, ncol = 3) &
  Seurat::NoLegend()

Cell type

We visualize cell type :

plot_list = lapply((c(paste0("RNA_pca_", ndims, "_umap"),
                      paste0("RNA_pca_", ndims, "_umap"),
                      paste0("harmony_", ndims, "_umap"),
                      paste0("harmony_", ndims, "_umap"))),
                   FUN = function(one_red) {
                     Seurat::DimPlot(sobj, group.by = "cell_type",
                                     reduction = one_red,
                                     cols = color_markers) +
                       Seurat::NoAxes() + ggplot2::ggtitle(one_red) +
                       ggplot2::theme(aspect.ratio = 1,
                                      plot.title = element_text(hjust = 0.5))
                   })

patchwork::wrap_plots(plot_list, nrow = 2) +
  patchwork::plot_layout(guides = "collect")

We make a representation split by origin to show cell types :

plot_list = aquarius::plot_split_dimred(sobj,
                                        reduction = name2D,
                                        split_by = "project_name",
                                        split_color = setNames(sample_info$color,
                                                               nm = sample_info$project_name),
                                        group_by = "cell_type",
                                        group_color = color_markers)

plot_list[[length(plot_list) + 1]] = patchwork::guide_area()

patchwork::wrap_plots(plot_list, ncol = 3) +
  patchwork::plot_layout(guides = "collect") &
  ggplot2::theme(legend.position = "right")

Cell cycle

We visualize cell cycle annotation, and BIRC5 and TOP2A expression levels :

plot_list = list()

# Seurat
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
                                 reduction = name2D) +
  Seurat::NoAxes() + ggplot2::labs(title = "Seurat annotation") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

# cyclone
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
                                 reduction = name2D) +
  Seurat::NoAxes() + ggplot2::labs(title = "cyclone annotation") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

# BIRC5
plot_list[[3]] = Seurat::FeaturePlot(sobj, features = "BIRC5",
                                     reduction = name2D) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

# TOP2A
plot_list[[4]] = Seurat::FeaturePlot(sobj, features = "TOP2A",
                                     reduction = name2D) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

patchwork::wrap_plots(plot_list, ncol = 2)

Save

We save the Seurat object :

saveRDS(sobj, file = paste0(out_dir, "/", save_name, "_sobj.rds"))

R Session

show

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
## 
## locale:
## [1] C
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] ComplexHeatmap_2.14.0 ggplot2_3.3.5         patchwork_1.1.2      
## [4] dplyr_1.0.7          
## 
## loaded via a namespace (and not attached):
##   [1] softImpute_1.4              graphlayouts_0.7.0         
##   [3] pbapply_1.4-2               lattice_0.20-41            
##   [5] haven_2.3.1                 vctrs_0.3.8                
##   [7] usethis_2.0.1               dynwrap_1.2.1              
##   [9] blob_1.2.1                  survival_3.2-13            
##  [11] prodlim_2019.11.13          dynutils_1.0.5             
##  [13] later_1.3.0                 DBI_1.1.1                  
##  [15] R.utils_2.11.0              SingleCellExperiment_1.8.0 
##  [17] rappdirs_0.3.3              uwot_0.1.8                 
##  [19] dqrng_0.2.1                 jpeg_0.1-8.1               
##  [21] zlibbioc_1.32.0             pspline_1.0-18             
##  [23] pcaMethods_1.78.0           mvtnorm_1.1-1              
##  [25] htmlwidgets_1.5.4           GlobalOptions_0.1.2        
##  [27] future_1.22.1               UpSetR_1.4.0               
##  [29] laeken_0.5.2                leiden_0.3.3               
##  [31] clustree_0.4.3              parallel_3.6.3             
##  [33] scater_1.14.6               irlba_2.3.3                
##  [35] DEoptimR_1.0-9              tidygraph_1.1.2            
##  [37] Rcpp_1.0.9                  readr_2.0.2                
##  [39] KernSmooth_2.23-17          carrier_0.1.0              
##  [41] promises_1.1.0              gdata_2.18.0               
##  [43] DelayedArray_0.12.3         limma_3.42.2               
##  [45] graph_1.64.0                RcppParallel_5.1.4         
##  [47] Hmisc_4.4-0                 fs_1.5.2                   
##  [49] RSpectra_0.16-0             fastmatch_1.1-0            
##  [51] ranger_0.12.1               digest_0.6.25              
##  [53] png_0.1-7                   sctransform_0.2.1          
##  [55] cowplot_1.0.0               DOSE_3.12.0                
##  [57] here_1.0.1                  TInGa_0.0.0.9000           
##  [59] ggvenn_0.1.9                ggraph_2.0.3               
##  [61] pkgconfig_2.0.3             GO.db_3.10.0               
##  [63] DelayedMatrixStats_1.8.0    gower_0.2.1                
##  [65] ggbeeswarm_0.6.0            iterators_1.0.12           
##  [67] DropletUtils_1.6.1          reticulate_1.26            
##  [69] clusterProfiler_3.14.3      SummarizedExperiment_1.16.1
##  [71] circlize_0.4.15             beeswarm_0.4.0             
##  [73] GetoptLong_1.0.5            xfun_0.35                  
##  [75] bslib_0.3.1                 zoo_1.8-10                 
##  [77] tidyselect_1.1.0            reshape2_1.4.4             
##  [79] purrr_0.3.4                 ica_1.0-2                  
##  [81] pcaPP_1.9-73                viridisLite_0.3.0          
##  [83] rtracklayer_1.46.0          rlang_1.0.2                
##  [85] hexbin_1.28.1               jquerylib_0.1.4            
##  [87] dyneval_0.9.9               glue_1.4.2                 
##  [89] RColorBrewer_1.1-2          matrixStats_0.56.0         
##  [91] stringr_1.4.0               lava_1.6.7                 
##  [93] europepmc_0.3               DESeq2_1.26.0              
##  [95] recipes_0.1.17              labeling_0.3               
##  [97] harmony_0.1.0               httpuv_1.5.2               
##  [99] class_7.3-17                BiocNeighbors_1.4.2        
## [101] DO.db_2.9                   annotate_1.64.0            
## [103] jsonlite_1.7.2              XVector_0.26.0             
## [105] bit_4.0.4                   mime_0.9                   
## [107] aquarius_0.1.5              Rsamtools_2.2.3            
## [109] gridExtra_2.3               gplots_3.0.3               
## [111] stringi_1.4.6               processx_3.5.2             
## [113] gsl_2.1-6                   bitops_1.0-6               
## [115] cli_3.0.1                   batchelor_1.2.4            
## [117] RSQLite_2.2.0               randomForest_4.6-14        
## [119] tidyr_1.1.4                 data.table_1.14.2          
## [121] rstudioapi_0.13             org.Mm.eg.db_3.10.0        
## [123] GenomicAlignments_1.22.1    nlme_3.1-147               
## [125] qvalue_2.18.0               scran_1.14.6               
## [127] locfit_1.5-9.4              scDblFinder_1.1.8          
## [129] listenv_0.8.0               ggthemes_4.2.4             
## [131] gridGraphics_0.5-0          R.oo_1.24.0                
## [133] dbplyr_1.4.4                BiocGenerics_0.32.0        
## [135] TTR_0.24.2                  readxl_1.3.1               
## [137] lifecycle_1.0.1             timeDate_3043.102          
## [139] ggpattern_0.3.1             munsell_0.5.0              
## [141] cellranger_1.1.0            R.methodsS3_1.8.1          
## [143] proxyC_0.1.5                visNetwork_2.0.9           
## [145] caTools_1.18.0              codetools_0.2-16           
## [147] Biobase_2.46.0              GenomeInfoDb_1.22.1        
## [149] vipor_0.4.5                 lmtest_0.9-38              
## [151] msigdbr_7.5.1               htmlTable_1.13.3           
## [153] triebeard_0.3.0             lsei_1.2-0                 
## [155] xtable_1.8-4                ROCR_1.0-7                 
## [157] BiocManager_1.30.10         scatterplot3d_0.3-41       
## [159] abind_1.4-5                 farver_2.0.3               
## [161] parallelly_1.28.1           RANN_2.6.1                 
## [163] askpass_1.1                 GenomicRanges_1.38.0       
## [165] RcppAnnoy_0.0.16            tibble_3.1.5               
## [167] ggdendro_0.1-20             cluster_2.1.0              
## [169] future.apply_1.5.0          Seurat_3.1.5               
## [171] dendextend_1.15.1           Matrix_1.3-2               
## [173] ellipsis_0.3.2              prettyunits_1.1.1          
## [175] lubridate_1.7.9             ggridges_0.5.2             
## [177] igraph_1.2.5                RcppEigen_0.3.3.7.0        
## [179] fgsea_1.12.0                remotes_2.4.2              
## [181] scBFA_1.0.0                 destiny_3.0.1              
## [183] VIM_6.1.1                   testthat_3.1.0             
## [185] htmltools_0.5.2             BiocFileCache_1.10.2       
## [187] yaml_2.2.1                  utf8_1.1.4                 
## [189] plotly_4.9.2.1              XML_3.99-0.3               
## [191] ModelMetrics_1.2.2.2        e1071_1.7-3                
## [193] foreign_0.8-76              withr_2.5.0                
## [195] fitdistrplus_1.0-14         BiocParallel_1.20.1        
## [197] xgboost_1.4.1.1             bit64_4.0.5                
## [199] foreach_1.5.0               robustbase_0.93-9          
## [201] Biostrings_2.54.0           GOSemSim_2.13.1            
## [203] rsvd_1.0.3                  memoise_2.0.0              
## [205] evaluate_0.18               forcats_0.5.0              
## [207] rio_0.5.16                  geneplotter_1.64.0         
## [209] tzdb_0.1.2                  caret_6.0-86               
## [211] ps_1.6.0                    DiagrammeR_1.0.6.1         
## [213] curl_4.3                    fdrtool_1.2.15             
## [215] fansi_0.4.1                 highr_0.8                  
## [217] urltools_1.7.3              xts_0.12.1                 
## [219] GSEABase_1.48.0             acepack_1.4.1              
## [221] edgeR_3.28.1                checkmate_2.0.0            
## [223] scds_1.2.0                  cachem_1.0.6               
## [225] npsurv_0.4-0                babelgene_22.3             
## [227] rjson_0.2.20                openxlsx_4.1.5             
## [229] ggrepel_0.9.1               clue_0.3-60                
## [231] rprojroot_2.0.2             stabledist_0.7-1           
## [233] tools_3.6.3                 sass_0.4.0                 
## [235] nichenetr_1.1.1             magrittr_2.0.1             
## [237] RCurl_1.98-1.2              proxy_0.4-24               
## [239] car_3.0-11                  ape_5.3                    
## [241] ggplotify_0.0.5             xml2_1.3.2                 
## [243] httr_1.4.2                  assertthat_0.2.1           
## [245] rmarkdown_2.18              boot_1.3-25                
## [247] globals_0.14.0              R6_2.4.1                   
## [249] Rhdf5lib_1.8.0              nnet_7.3-14                
## [251] RcppHNSW_0.2.0              progress_1.2.2             
## [253] genefilter_1.68.0           statmod_1.4.34             
## [255] gtools_3.8.2                shape_1.4.6                
## [257] HDF5Array_1.14.4            BiocSingular_1.2.2         
## [259] rhdf5_2.30.1                splines_3.6.3              
## [261] AUCell_1.8.0                carData_3.0-4              
## [263] colorspace_1.4-1            generics_0.1.0             
## [265] stats4_3.6.3                base64enc_0.1-3            
## [267] dynfeature_1.0.0            smoother_1.1               
## [269] gridtext_0.1.1              pillar_1.6.3               
## [271] tweenr_1.0.1                sp_1.4-1                   
## [273] ggplot.multistats_1.0.0     rvcheck_0.1.8              
## [275] GenomeInfoDbData_1.2.2      plyr_1.8.6                 
## [277] gtable_0.3.0                zip_2.2.0                  
## [279] knitr_1.41                  latticeExtra_0.6-29        
## [281] biomaRt_2.42.1              IRanges_2.20.2             
## [283] fastmap_1.1.0               ADGofTest_0.3              
## [285] copula_1.0-0                doParallel_1.0.15          
## [287] AnnotationDbi_1.48.0        vcd_1.4-8                  
## [289] babelwhale_1.0.1            openssl_1.4.1              
## [291] scales_1.1.1                backports_1.2.1            
## [293] S4Vectors_0.24.4            ipred_0.9-12               
## [295] enrichplot_1.6.1            hms_1.1.1                  
## [297] ggforce_0.3.1               Rtsne_0.15                 
## [299] shiny_1.7.1                 numDeriv_2016.8-1.1        
## [301] polyclip_1.10-0             lazyeval_0.2.2             
## [303] Formula_1.2-3               tsne_0.1-3                 
## [305] crayon_1.3.4                MASS_7.3-54                
## [307] pROC_1.16.2                 viridis_0.5.1              
## [309] dynparam_1.0.0              rpart_4.1-15               
## [311] zinbwave_1.8.0              compiler_3.6.3             
## [313] ggtext_0.1.0